📊 Customer-Segmentation-Dashboard-Power-BI

A professional RFM-based Customer Segmentation Dashboard built in Power BI, replicating a dark-themed HTML prototype. The dashboard segments customers into 7 behavioral groups using Recency, Frequency, and Monetary (RFM) analysis to drive targeted marketing actions.

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🖼️ Dashboard Preview

The dashboard features:

• 5 KPI cards with dynamic delta indicators
• Revenue by Segment bar chart
• Customer Distribution donut chart
• Recency vs Spend bubble chart
• RFM Score Distribution stacked bar chart
• Segment Performance table with mini progress bars

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📁 Repository Structure

├── dashboard.html                        # Original HTML prototype (reference design)
├── SegmentData.csv                       # Source data for all 7 customer segments
├── CustomerSegmentation_PBI_Theme.json   # Power BI dark theme (import-ready)
├── CustomerSegmentation.pbix            # Power BI report file
└── README.md                            # Project documentation

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📊 Dashboard Visuals

KPI Cards (5)

KPI	Value	Delta
Total Customers	9,021	▲ +8.2% vs last year
Total Revenue	$5,592K	▲ +12.4% vs last year
Avg Order Value	$105	▲ +3.1% vs last year
Avg Recency (days)	543d	▼ +6 days vs last year
Champions Revenue Share	41%	▲ +2.1pp vs last year

Charts

• Revenue by Segment — Horizontal bar chart, each bar colored by segment
• Customer Distribution — Donut chart (62% inner radius) showing customer count share
• Recency vs Spend — Bubble chart where bubble size = avg frequency
• RFM Score Distribution — Stacked bar chart showing R, F, M scores per segment

Segment Performance Table

Columns: Segment pill · Customers · Revenue · Revenue Share % (with mini bar) · Avg AOV · Avg Recency · Action

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🚀 Getting Started

Prerequisites

• Power BI Desktop (latest version recommended)
• Basic knowledge of Power BI visuals and DAX

Setup Steps

1. Clone the repository

git clone https://github.com/iamvikash28/Customer-Segmentation-Dashboard-Power-BI.git
cd Customer-Segmentation-Dashboard-Power-BI

2. Import the Theme

1. Open Power BI Desktop
2. Go to View → Themes → Browse for themes
3. Select CustomerSegmentation_PBI_Theme.json

3. Load the Data

1. Go to Home → Get Data → Text/CSV
2. Select segments_labeled.csv
3. Click Load
4. Set column data types in Power Query:

Column	Type
Segment, Action	Text
Customers, Revenue, Avg AOV, Avg Recency	Whole Number
Avg Frequency, R Score, F Score, M Score	Decimal Number

4. Open the Report

• Open dashboard.pbix in Power BI Desktop

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🧮 Key DAX Measures

-- Total Customers
Total Customers = SUM('SegmentData'[Customers])

-- Total Revenue
Total Revenue = SUM('SegmentData'[Revenue])

-- Weighted Avg Order Value
Avg Order Value =
DIVIDE(
    SUMX('SegmentData', 'SegmentData'[Avg AOV] * 'SegmentData'[Customers]),
    SUM('SegmentData'[Customers])
)

-- Weighted Avg Recency
Avg Recency Days =
DIVIDE(
    SUMX('SegmentData', 'SegmentData'[Avg Recency] * 'SegmentData'[Customers]),
    SUM('SegmentData'[Customers])
)

-- Champions Revenue Share
Champions Revenue Share =
DIVIDE(
    CALCULATE(SUM('SegmentData'[Revenue]), 'SegmentData'[Segment] = "Champions"),
    CALCULATE(SUM('SegmentData'[Revenue]), ALL('SegmentData'))
)

-- Revenue Share % (per segment)
Revenue Share % =
DIVIDE(
    SUM('SegmentData'[Revenue]),
    CALCULATE(SUM('SegmentData'[Revenue]), ALL('SegmentData'))
)

-- Avg Recency with "d" suffix (display)
Avg Recency Display =
FORMAT(AVERAGE('SegmentData'[Avg Recency]), "0") & "d"

-- Segment bar color (for conditional formatting)
Revenue Share Bar Color =
SWITCH(
    SELECTEDVALUE('SegmentData'[Segment]),
    "Champions",             "#378ADD",
    "Loyal customers",       "#1D9E75",
    "Potential loyalists",   "#7F77DD",
    "New customers",         "#EF9F27",
    "At-risk customers",     "#D85A30",
    "Frequent low-spenders", "#D4537E",
    "Lost / dormant",        "#888780",
    "#378ADD"
)

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📦 Data Source

The segments_labeled.csv contains 7 rows (one per segment) with the following fields:

Field	Description
Segment	Customer segment name
Customers	Number of customers in segment
Revenue	Total revenue generated ($)
Avg AOV	Average order value ($)
Avg Recency	Average days since last purchase
Avg Frequency	Average number of purchases
R Score	Recency score (1–5)
F Score	Frequency score (1–5)
M Score	Monetary score (1–5)
Action	Recommended marketing action

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👤 Author

Vikash Verma Aspiring Data Analyst | Excel · SQL · Power BI · Python | E-mail- vikashverma566@gmail.com

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